Adaptive regularised l2‐boosting on clustered sparse coefficients for single image super‐resolution. Issue 7 (13th July 2017)

• Record Type: Journal Article
• Authors: Han, Yulan; Zhao, Yongping; Yu, Haifeng
• Is Part Of: IET computer vision. Volume 11:Issue 7(2017)
• Page Start: 517
• Page End: 529
• Publication Date: 2017-07-13
• Subjects: image resolution -- image reconstruction -- learning (artificial intelligence) -- pattern clustering; adaptive regularised l2-boosting algorithm -- clustered sparse coefficients -- single image super-resolution -- mapping function learning -- low-resolution image patch -- high-resolution image patch -- LR image patch -- HR image patch -- image reconstruction; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2016.0274
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : In this study, the authors propose a novel approach for single image super‐resolution. Their method is based on the idea of learning a mapping function, which can reveal the intrinsic relationship between sparse coefficients of low‐resolution (LR) and high‐resolution (HR) image patch pairs with respect to their individual dictionaries. Adaptive regularised l 2 ‐boosting algorithm is proposed to learn this type of mapping function. Specifically, to reduce time consumption, the authors cluster training patches into several clusters. Within each cluster, a pair of dictionaries for LR and HR image patches is jointly trained. Adaptive regularised l 2 ‐boosting algorithm is then employed to obtain the function. Thus, in a reconstruction stage, for each given input LR image patch, the authors can effectively estimate its corresponding HR image patch. Their extensive experimental results demonstrated that the proposed method achieves a performance of similar quality performance to that of the top methods.